Visual Intelligence congratulates Hyeongji Kim for getting her research paper "ProxyDR: Deep Hyperspherical Metric Learning with Distance Ratio-Based Formulation" accepted for a coming European Conference on Computer Vision (ECCV) Workshop"
- I am excited to present our paper at this event, as it will provide a great opportunity to share our findings and exchange ideas with other researchers in the field, says postdoctoral researcher and main author, Hyeongji Kim.
By Petter Bjørklund, Communication Advisor at SFI Visual Intelligence.
Visual Intelligence congratulates Hyeongji Kim for getting her research paper "ProxyDR: Deep Hyperspherical Metric Learning with Distance Ratio-Based Formulation" accepted for the coming European Conference on Computer Vision (ECCV) Workshop in September. The submitted work offers a novel approach to metric learning called "ProxyDR".
ECCV is reputed as a premier research conference in Computer Vision and Machine Learning, alongside top research conferences like CVPR and ICCV. The workshop is titled "Beyond Euclidean: Hyperbolic and Hyperspherical Learning for Computer Vision".
- I am excited to present our paper at this event, as it will provide a great opportunity to share our findings and exchange ideas with other researchers in the field. I look forward to demonstrating the strong potential of the distance ratio-based formulation, originally developed in my previous work, and promoting its wider adoption in metric learning, says Kim.
Kim is a postdoctoral research fellow at UiT The Arctic University of Norway and SFI Visual Intelligence. She completed her doctoral degree at the University of Bergen in October 2023.
The goal of metric learning is to learn an embedding space where similar data points are positioned close together while different points are spaced farther apart. According to Kim, this is essential for applications like image retrieval and few-shot classification.
However, the conventional squared distance softmax formulation often results in class representatives being too uniformly distributed, which hinders the model's ability to capture complex relationships between classes.
- To address these limitations, we propose a distance ratio (DR)-based methodology called ProxyDR, which enhances the learning of semantic relationships among classes. Additionally, ProxyDR integrates proxies as class representatives, which simplifies the learning process and improves efficiency.
She adds that through extensive evaluations on diverse datasets, she and the co-authors demonstrate its superior capability to capture complex semantic structures without compromising classification accuracy.
The majority of the work was done while Kim worked on her PhD thesis. Another additional experiment and rewriting was done in collaboration with Visual Intelligence researchers Michael Kampffmeyer and Changkyu Choi.
List of authors: Hyeongji Kim, Changkyu Choi, Michael Kampffmeyer, Terje Berge, Pekka Parviainen, Ketil Malde. Malde and Parviainen supervised Kim's PhD project.
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